In [1]:
import pandas as pd
import numpy as np
import geopandas as gpd
In [2]:
beats=gpd.read_file("https://services.arcgis.com/ZOyb2t4B0UYuYNYH/arcgis/rest/services/Current_Beats/FeatureServer/0/query?outFields=*&where=1%3D1&f=geojson")
In [3]:
nbh=gpd.read_file("https://services.arcgis.com/ZOyb2t4B0UYuYNYH/arcgis/rest/services/nma_nhoods_sub/FeatureServer/0/query?outFields=*&where=1%3D1&f=geojson")
In [4]:
incidents = pd.read_json("https://data.seattle.gov/resource/tazs-3rd5.json?$query=SELECT%0A%20%20%60report_number%60%2C%0A%20%20%60report_date_time%60%2C%0A%20%20%60offense_id%60%2C%0A%20%20%60offense_date%60%2C%0A%20%20%60nibrs_group_a_b%60%2C%0A%20%20%60nibrs_crime_against_category%60%2C%0A%20%20%60offense_sub_category%60%2C%0A%20%20%60shooting_type_group%60%2C%0A%20%20%60block_address%60%2C%0A%20%20%60latitude%60%2C%0A%20%20%60longitude%60%2C%0A%20%20%60beat%60%2C%0A%20%20%60precinct%60%2C%0A%20%20%60sector%60%2C%0A%20%20%60neighborhood%60%2C%0A%20%20%60reporting_area%60%2C%0A%20%20%60offense_category%60%2C%0A%20%20%60nibrs_offense_code_description%60%2C%0A%20%20%60nibrs_offense_code%60%0AWHERE%0A%20%20%60report_date_time%60%0A%20%20%20%20BETWEEN%20%222025-11-03T00%3A00%3A00%22%20%3A%3A%20floating_timestamp%0A%20%20%20%20AND%20%222025-11-07T22%3A45%3A00%22%20%3A%3A%20floating_timestamp")
incidents
Out[4]:
report_number report_date_time offense_id offense_date nibrs_group_a_b nibrs_crime_against_category offense_sub_category shooting_type_group block_address latitude longitude beat precinct sector neighborhood reporting_area offense_category nibrs_offense_code_description nibrs_offense_code
0 2025-321958 2025-11-03 00:20:01 67167585930 2025-11-02T23:12:00.000 A PERSON ASSAULT OFFENSES - REDACTED REDACTED REDACTED S2 South S BRIGHTON/DUNLAP REDACTED ALL OTHER Intimidation 13C
1 2025-321962 2025-11-03 00:58:02 67167929818 2025-11-02T23:22:00.000 A PERSON VIOLATION OF NO CONTACT ORDER - REDACTED REDACTED REDACTED S1 South S MID BEACON HILL REDACTED ALL OTHER Violation of No Contact Orders 500
2 2025-321994 2025-11-03 01:05:38 67167876122 2025-11-03T00:25:00.000 B NOT_A_CRIME 999 - 103XX BLOCK OF AURORA AVE N 47.70342166 -122.344674750346 N3 North N NORTHGATE 1967 ALL OTHER Not Reportable to NIBRS 999
3 2025-321990 2025-11-03 01:12:58 67167892721 2025-11-03T00:19:00.000 A PROPERTY ROBBERY - 112XX BLOCK OF AURORA AVE N 47.71119796 -122.344795214832 N3 North N NORTHGATE 11341 VIOLENT CRIME Robbery 120
4 2025-321981 2025-11-03 01:33:07 67167946601 2025-11-03T00:01:00.000 A PERSON ASSAULT OFFENSES - 29XX BLOCK OF S COLUMBIAN WAY 47.56262374 -122.295274743911 R2 South R CLAREMONT/RAINIER VISTA 10168 ALL OTHER Simple Assault 13B
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
992 2025-326543 2025-11-06 22:20:01 67242117613 2025-11-06T21:10:00.000 B ANY ALL OTHER - 1XX BLOCK OF 6TH AVE S 47.60129044 -122.326352376011 K3 West K CHINATOWN/INTERNATIONAL DISTRICT 822 ALL OTHER All Other Offenses 90Z
993 2025-326543 2025-11-06 22:20:01 67242080867 2025-11-06T21:10:00.000 A PERSON ASSAULT OFFENSES - 1XX BLOCK OF 6TH AVE S 47.60129044 -122.326352376011 K3 West K CHINATOWN/INTERNATIONAL DISTRICT 822 ALL OTHER Simple Assault 13B
994 2025-326526 2025-11-06 22:20:45 67242088334 2025-11-06T20:54:00.000 A PROPERTY BURGLARY - 9XX BLOCK OF NW MARKET ST 47.66866158 -122.369682040098 B2 North B BALLARD SOUTH 4700 PROPERTY CRIME Burglary/Breaking & Entering 220
995 2025-326592 2025-11-06 22:51:55 67242211063 2025-11-06T22:30:00.000 B ANY ALL OTHER - 1XX BLOCK OF BLANCHARD ST 47.61293883 -122.3447833 D1 West D BELLTOWN 8886 ALL OTHER All Other Offenses 90Z
996 2025-326576 2025-11-06 23:00:37 67242247965 2025-11-06T22:00:00.000 A PERSON ASSAULT OFFENSES - 1XX BLOCK OF NE 94TH ST 47.69684659 -122.327638372321 L2 North L - 7455 ALL OTHER Simple Assault 13B

997 rows × 19 columns

In [5]:
beats.crs
beats.head()
Out[5]:
objectid beat first_precinct sector Shape__Area Shape__Length geometry
0 1 99 None 99 2.087731e+08 280794.869337 MULTIPOLYGON (((-122.38 47.749, -122.38 47.748...
1 2 B1 N B 3.888917e+07 30766.974987 POLYGON ((-122.41 47.676, -122.41 47.675, -122...
2 3 B2 N B 5.514478e+07 32647.183200 POLYGON ((-122.37 47.676, -122.37 47.676, -122...
3 4 B3 N B 5.961233e+07 36973.284491 POLYGON ((-122.34 47.669, -122.34 47.669, -122...
4 5 C1 E C 3.255283e+07 37950.240764 POLYGON ((-122.31 47.614, -122.31 47.614, -122...
In [6]:
incidents.head()
Out[6]:
report_number report_date_time offense_id offense_date nibrs_group_a_b nibrs_crime_against_category offense_sub_category shooting_type_group block_address latitude longitude beat precinct sector neighborhood reporting_area offense_category nibrs_offense_code_description nibrs_offense_code
0 2025-321958 2025-11-03 00:20:01 67167585930 2025-11-02T23:12:00.000 A PERSON ASSAULT OFFENSES - REDACTED REDACTED REDACTED S2 South S BRIGHTON/DUNLAP REDACTED ALL OTHER Intimidation 13C
1 2025-321962 2025-11-03 00:58:02 67167929818 2025-11-02T23:22:00.000 A PERSON VIOLATION OF NO CONTACT ORDER - REDACTED REDACTED REDACTED S1 South S MID BEACON HILL REDACTED ALL OTHER Violation of No Contact Orders 500
2 2025-321994 2025-11-03 01:05:38 67167876122 2025-11-03T00:25:00.000 B NOT_A_CRIME 999 - 103XX BLOCK OF AURORA AVE N 47.70342166 -122.344674750346 N3 North N NORTHGATE 1967 ALL OTHER Not Reportable to NIBRS 999
3 2025-321990 2025-11-03 01:12:58 67167892721 2025-11-03T00:19:00.000 A PROPERTY ROBBERY - 112XX BLOCK OF AURORA AVE N 47.71119796 -122.344795214832 N3 North N NORTHGATE 11341 VIOLENT CRIME Robbery 120
4 2025-321981 2025-11-03 01:33:07 67167946601 2025-11-03T00:01:00.000 A PERSON ASSAULT OFFENSES - 29XX BLOCK OF S COLUMBIAN WAY 47.56262374 -122.295274743911 R2 South R CLAREMONT/RAINIER VISTA 10168 ALL OTHER Simple Assault 13B
In [7]:
nbh_sort = nbh.sort_values('Shape__Area', ascending = False)
print(nbh_sort['S_HOOD'])

93    Industrial District
23            Lawton Park
53        Mid-Beacon Hill
41             Georgetown
40                   SODO
             ...         
37         Yesler Terrace
92         Denny Triangle
70            Portage Bay
20               Westlake
32            Pike-Market
Name: S_HOOD, Length: 94, dtype: object
In [8]:
len(beats)
Out[8]:
55
In [9]:
incidents['longitude'].describe()
Out[9]:
count          997
unique         719
top       REDACTED
freq           112
Name: longitude, dtype: object
In [10]:
incidents['latitude'].describe()
Out[10]:
count          997
unique         696
top       REDACTED
freq           112
Name: latitude, dtype: object
In [11]:
incidents2 = incidents[(incidents['longitude'] != 'REDACTED')]
In [12]:
incidents2 = incidents2.astype({'longitude': float, 'latitude':float})
In [13]:
# Geopandas.GeoDataFrame() method will conver the data to spatial form, Geo dataframe with points_from_xy method.
spd_crime=gpd.GeoDataFrame(incidents2, geometry=gpd.points_from_xy(incidents2.longitude, incidents2.latitude), crs='EPSG:4326')
In [14]:
spd_crime.plot()
Out[14]:
<Axes: >
No description has been provided for this image
In [15]:
spd_crime['longitude'].describe()
Out[15]:
count    885.000000
mean    -121.097621
std       12.180086
min     -122.411979
25%     -122.349529
50%     -122.328110
75%     -122.312761
max       -1.000000
Name: longitude, dtype: float64
In [16]:
spd_crime2 = spd_crime[(spd_crime['longitude'] != -1)]
In [17]:
spd_crime2.plot()
Out[17]:
<Axes: >
No description has been provided for this image
In [18]:
spd_crime3 = spd_crime2[[ 'report_number','report_date_time','offense_id','offense_category' ,'latitude', 'longitude','geometry']]
spd_crime3
Out[18]:
report_number report_date_time offense_id offense_category latitude longitude geometry
2 2025-321994 2025-11-03 01:05:38 67167876122 ALL OTHER 47.703422 -122.344675 POINT (-122.34 47.703)
3 2025-321990 2025-11-03 01:12:58 67167892721 VIOLENT CRIME 47.711198 -122.344795 POINT (-122.34 47.711)
4 2025-321981 2025-11-03 01:33:07 67167946601 ALL OTHER 47.562624 -122.295275 POINT (-122.3 47.563)
5 2025-321893 2025-11-03 02:40:08 67168135239 ALL OTHER 47.536403 -122.392255 POINT (-122.39 47.536)
6 2025-321893 2025-11-03 02:40:08 67168146760 ALL OTHER 47.536403 -122.392255 POINT (-122.39 47.536)
... ... ... ... ... ... ... ...
992 2025-326543 2025-11-06 22:20:01 67242117613 ALL OTHER 47.601290 -122.326352 POINT (-122.33 47.601)
993 2025-326543 2025-11-06 22:20:01 67242080867 ALL OTHER 47.601290 -122.326352 POINT (-122.33 47.601)
994 2025-326526 2025-11-06 22:20:45 67242088334 PROPERTY CRIME 47.668662 -122.369682 POINT (-122.37 47.669)
995 2025-326592 2025-11-06 22:51:55 67242211063 ALL OTHER 47.612939 -122.344783 POINT (-122.34 47.613)
996 2025-326576 2025-11-06 23:00:37 67242247965 ALL OTHER 47.696847 -122.327638 POINT (-122.33 47.697)

876 rows × 7 columns

In [19]:
import folium
In [20]:
map = folium.Map(location=[47.6577792,-122.3031197], zoom_start=10)
map
Out[20]:
Make this Notebook Trusted to load map: File -> Trust Notebook
In [21]:
map1 = folium.Map(location=[47.6577792,-122.3031197], zoom_start=10)
folium.GeoJson(nbh,
            popup=folium.GeoJsonPopup(fields=['L_HOOD'])   
              ).add_to(map1)
map1
Out[21]:
Make this Notebook Trusted to load map: File -> Trust Notebook
In [22]:
map1.save("index.html")
In [23]:
map2 = folium.Map(location=[47.6577792,-122.3031197], zoom_start=10)
folium.GeoJson(beats,
              popup=folium.GeoJsonPopup(fields=['beat'])  
              ).add_to(map2)
map2
Out[23]:
Make this Notebook Trusted to load map: File -> Trust Notebook
In [24]:
spd_crime3['report_date_time'] = spd_crime3['report_date_time'].dt.strftime('%Y-%m-%d %H:%M:%S')
map3 = folium.Map(location=[47.6577792,-122.3031197], zoom_start=10)
folium.GeoJson(spd_crime3).add_to(map3)
map3

c:\Users\turne\anaconda3\Lib\site-packages\geopandas\geodataframe.py:1968: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  super().__setitem__(key, value)
Out[24]:
Make this Notebook Trusted to load map: File -> Trust Notebook
In [25]:
offense_type = spd_crime3.offense_category.unique().tolist()
offense_type
Out[25]:
['ALL OTHER', 'VIOLENT CRIME', 'PROPERTY CRIME']
In [26]:
colors = {'PROPERTY CRIME':'red', 'VIOLENT CRIME': 'darkpurple', 'ALL OTHER':'lightblue'}
In [27]:
map4 = folium.Map(location=[47.6577792,-122.3031197], zoom_start=10)

# Add markers with colors based on category
for index, row in spd_crime3.iterrows():
    folium.Marker(
        location=[row['latitude'], row['longitude']],
        popup=row['offense_category'],
        icon=folium.Icon(color=colors.get(row['offense_category'], 'gray')) # Default to gray if category not found
    ).add_to(map4)

map4
Out[27]:
Make this Notebook Trusted to load map: File -> Trust Notebook
In [28]:
curr_date = pd.to_datetime('today', utc=True).tz_convert('America/Los_Angeles').date()
curr_date
Out[28]:
datetime.date(2025, 11, 7)
In [29]:
incidents_today =  spd_crime3[pd.to_datetime(spd_crime3['report_date_time']).dt.date == curr_date]
incidents_today
Out[29]:
report_number report_date_time offense_id offense_category latitude longitude geometry
In [30]:
map5 = folium.Map(location=[47.6577792,-122.3031197], zoom_start=10)

folium.GeoJson(incidents_today).add_to(map5)

map5
Out[30]:
Make this Notebook Trusted to load map: File -> Trust Notebook
In [31]:
incidents_yesterday =  spd_crime3[pd.to_datetime(spd_crime3['report_date_time']).dt.date == curr_date - pd.Timedelta(days=1)]
incidents_yesterday
Out[31]:
report_number report_date_time offense_id offense_category latitude longitude geometry
854 2025-325470 2025-11-06 00:15:06 67217550137 ALL OTHER 47.632927 -122.361016 POINT (-122.36 47.633)
855 2025-325470 2025-11-06 00:15:06 67217554931 ALL OTHER 47.632927 -122.361016 POINT (-122.36 47.633)
856 2025-325470 2025-11-06 00:15:06 67217557249 ALL OTHER 47.632927 -122.361016 POINT (-122.36 47.633)
857 2025-325482 2025-11-06 00:55:17 67217736640 ALL OTHER 47.595837 -122.326374 POINT (-122.33 47.596)
858 2025-325479 2025-11-06 01:30:41 67217866391 ALL OTHER 47.509647 -122.265438 POINT (-122.27 47.51)
... ... ... ... ... ... ... ...
992 2025-326543 2025-11-06 22:20:01 67242117613 ALL OTHER 47.601290 -122.326352 POINT (-122.33 47.601)
993 2025-326543 2025-11-06 22:20:01 67242080867 ALL OTHER 47.601290 -122.326352 POINT (-122.33 47.601)
994 2025-326526 2025-11-06 22:20:45 67242088334 PROPERTY CRIME 47.668662 -122.369682 POINT (-122.37 47.669)
995 2025-326592 2025-11-06 22:51:55 67242211063 ALL OTHER 47.612939 -122.344783 POINT (-122.34 47.613)
996 2025-326576 2025-11-06 23:00:37 67242247965 ALL OTHER 47.696847 -122.327638 POINT (-122.33 47.697)

125 rows × 7 columns

In [32]:
map6 = folium.Map(location=[47.6577792,-122.3031197], zoom_start=10)
folium.GeoJson(incidents_yesterday,
              popup=folium.GeoJsonPopup(fields=['offense_category'])
              ).add_to(map6)
map6
Out[32]:
Make this Notebook Trusted to load map: File -> Trust Notebook
In [33]:
from datetime import date
# the number of crime on mmddyy at the Beat U2, replace mmddyy to a date in your dataset
crime_251106=spd_crime3[pd.to_datetime(spd_crime3['report_date_time']).dt.date == date(2025, 11, 6)]
beat_u2=beats[beats['beat']=='U2']
crime_u2 = crime_251106.overlay(beat_u2, how='intersection')
map7 = folium.Map(location=[47.6577792,-122.3031197], zoom_start=10)
folium.GeoJson(crime_u2,
              popup=folium.GeoJsonPopup(fields=['report_date_time','offense_category'])
              ).add_to(map7)
map7
Out[33]:
Make this Notebook Trusted to load map: File -> Trust Notebook
In [34]:
spd_crime3['date'] = pd.to_datetime(spd_crime3['report_date_time']).dt.date
daily_crime_counts = spd_crime3.groupby(['date'])['offense_id'].count().reset_index()
daily_crime_counts

c:\Users\turne\anaconda3\Lib\site-packages\geopandas\geodataframe.py:1968: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  super().__setitem__(key, value)
Out[34]:
date offense_id
0 2025-11-03 283
1 2025-11-04 271
2 2025-11-05 197
3 2025-11-06 125
In [35]:
daily_crime_counts = daily_crime_counts.rename(columns={"offense_id":"n_incidents"})
daily_crime_counts.plot.bar(x='date', y='n_incidents')
Out[35]:
<Axes: xlabel='date'>
No description has been provided for this image
In [36]:
crime_110325=spd_crime3[pd.to_datetime(spd_crime3['report_date_time']).dt.date == date(2025, 11, 3)]
crime_nbh_110325 = nbh.sjoin(crime_110325, how='left', predicate='intersects')
crime_nbh_110325 = crime_nbh_110325.dissolve(by='S_HOOD', aggfunc='count').reset_index()
In [37]:
crime_nbh_110325 = crime_nbh_110325[['S_HOOD','geometry','OBJECTID']].rename(columns={"OBJECTID":"n_incidents"})
crime_110325
Out[37]:
report_number report_date_time offense_id offense_category latitude longitude geometry date
2 2025-321994 2025-11-03 01:05:38 67167876122 ALL OTHER 47.703422 -122.344675 POINT (-122.34 47.703) 2025-11-03
3 2025-321990 2025-11-03 01:12:58 67167892721 VIOLENT CRIME 47.711198 -122.344795 POINT (-122.34 47.711) 2025-11-03
4 2025-321981 2025-11-03 01:33:07 67167946601 ALL OTHER 47.562624 -122.295275 POINT (-122.3 47.563) 2025-11-03
5 2025-321893 2025-11-03 02:40:08 67168135239 ALL OTHER 47.536403 -122.392255 POINT (-122.39 47.536) 2025-11-03
6 2025-321893 2025-11-03 02:40:08 67168146760 ALL OTHER 47.536403 -122.392255 POINT (-122.39 47.536) 2025-11-03
... ... ... ... ... ... ... ... ...
309 2025-322878 2025-11-03 22:54:04 67181560704 ALL OTHER 47.667785 -122.281543 POINT (-122.28 47.668) 2025-11-03
310 2025-322929 2025-11-03 22:59:55 67183503429 ALL OTHER 47.692091 -122.302392 POINT (-122.3 47.692) 2025-11-03
311 2025-322929 2025-11-03 22:59:55 67181595406 PROPERTY CRIME 47.700722 -122.302935 POINT (-122.3 47.701) 2025-11-03
312 2025-323073 2025-11-03 23:45:34 67182749478 PROPERTY CRIME 47.622595 -122.312637 POINT (-122.31 47.623) 2025-11-03
313 2025-323039 2025-11-03 23:49:37 67182851532 PROPERTY CRIME 47.616987 -122.320826 POINT (-122.32 47.617) 2025-11-03

283 rows × 8 columns

In [38]:
from branca.colormap import linear

colormap = linear.PuRd_05.scale(
    crime_nbh_110325.n_incidents.min(),  crime_nbh_110325.n_incidents.max()
)

data_dict =  crime_nbh_110325.set_index("S_HOOD")["n_incidents"]

map8 = folium.Map(location=[47.6577792,-122.3031197], zoom_start=11)
folium.GeoJson(crime_nbh_110325,
    name="incidents",
    style_function=lambda feature: {
        "fillColor": colormap(data_dict[feature['properties']["S_HOOD"]]),
        "color": "black",
        "weight": 1,
        "dashArray": "5, 5",
        "fillOpacity": 0.9,
    },
              popup=folium.GeoJsonPopup(fields=['S_HOOD','n_incidents'])
              ).add_to(map8)

folium.LayerControl().add_to(map8)
colormap.add_to(map8)
map8
Out[38]:
Make this Notebook Trusted to load map: File -> Trust Notebook
In [39]:
from shapely.geometry import Point
ps_loc = Point(-122.3313539, 47.6027217) 
ps = gpd.GeoDataFrame(geometry=[ps_loc], crs=4326)
ps_utm = ps.to_crs('epsg:32610')
ps_buffer = ps_utm.buffer(500)
ps_buffer_4326 = ps_buffer.to_crs('epsg:4326') 
ps_buffer_4326
Out[39]:
0    POLYGON ((-122.32 47.603, -122.32 47.602, -122...
dtype: geometry
In [40]:
map9 = folium.Map(location=[47.6008863,-122.3377], zoom_start=14)
folium.GeoJson(
        data=ps_buffer_4326.to_json(),
   ).add_to(map9)
folium.Marker(
        location=[47.6027217,-122.3313539],
  ).add_to(map9)
map9
Out[40]:
Make this Notebook Trusted to load map: File -> Trust Notebook
In [41]:
ps_buffer_4326 = gpd.GeoDataFrame(geometry=ps_buffer_4326)
In [42]:
crime_ps =  spd_crime3.overlay(ps_buffer_4326, how='intersection')
In [43]:
crime_ps['date'] = crime_ps['date'].astype(str)
map10 = folium.Map(location=[47.6008863,-122.3377], zoom_start=14)
folium.GeoJson(crime_ps,
               popup=folium.GeoJsonPopup(fields=['offense_category']) # turn on the popop to display the offense category.
  ).add_to(map10)
map10
Out[43]:
Make this Notebook Trusted to load map: File -> Trust Notebook
In [44]:
import ipywidgets as widgets
from IPython.display import display

spd_crime3['report_date_time'] = spd_crime3['report_date_time'].astype('str')
spd_crime3['date'] = spd_crime3['date'].astype('str')
map11 = folium.Map(location=[47.6008863,-122.3377], zoom_start=10)
out = widgets.Output(layout={'border': '1px solid black'})

# Create a Dropdown widget
dropdown = widgets.Dropdown(
    options=nbh['S_HOOD'].values.tolist(),
    value=nbh['S_HOOD'].values[0],  # Default selected value
    description='Select',
    disabled=False
)

def on_dropdown_change(change):
    out.clear_output()
    with out:
        map11_n = folium.Map(location=[47.6008863,-122.3377], zoom_start=10)
        my_gdf = spd_crime3.overlay(nbh[nbh['S_HOOD']==dropdown.value], how='intersection')
        layer1 = folium.GeoJson(my_gdf).add_to(map11_n)
        n_mygdf = len(my_gdf)
        print ("The number of crime at "+ dropdown.value + f" is {n_mygdf}")
        display(map11_n)
        

dropdown.observe(on_dropdown_change, names='value')
display(dropdown)

with out:
    display(map11)

out

c:\Users\turne\anaconda3\Lib\site-packages\geopandas\geodataframe.py:1968: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  super().__setitem__(key, value)
c:\Users\turne\anaconda3\Lib\site-packages\geopandas\geodataframe.py:1968: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  super().__setitem__(key, value)

Dropdown(description='Select', options=('Loyal Heights', 'Ballard', 'Whittier Heights', 'West Woodland', 'Phin…
Output(layout=Layout(border='1px solid black'))
In [45]:
spd_crime3['report_date_time'] = pd.to_datetime(spd_crime3['report_date_time']).sort_values(ascending=True).dt.date
data = []
for _, d in spd_crime3.groupby('report_date_time'):
   data.append([[row['latitude'], row['longitude']] for _, row in d.iterrows()])

c:\Users\turne\anaconda3\Lib\site-packages\geopandas\geodataframe.py:1968: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  super().__setitem__(key, value)
In [46]:
from datetime import datetime, timedelta

time_index = [
    (min(spd_crime3['report_date_time']) + k * timedelta(1)).strftime("%Y-%m-%d") for k in range(len(data))
]
In [47]:
from folium.plugins import HeatMapWithTime

map12 = folium.Map(location=[47.6008863,-122.3377], zoom_start=10)

hm = folium.plugins.HeatMapWithTime(data, index=time_index, auto_play=True, max_opacity=0.3)

hm.add_to(map12)

display(map12)
Make this Notebook Trusted to load map: File -> Trust Notebook